In the field of image processing, the use of motion detection technology can automatically detect motion scenes in videos, which can reduce the cost of manual monitoring and increase monitoring efficiency and effectiveness. As the foundation for motion detection recording technology and an important component of smart video cameras, motion detection technology has been embedded into the camera firmware of an increasing number of smart video cameras.
There are three types of conventional motion detection methods: the optical flow method, the inter-frame difference method, and the background subtraction method. In the optical flow method, each pixel point in the image is assigned a velocity vector to form an image motion field; at a given moment of the motion, the points in the image have a one-to-one relationship with the points on the three-dimensional object, and this relationship can be obtained from the projection relationship; a dynamic analysis on the image can be performed based on the characteristics of the velocity vector of each pixel point. If no moving object is present in the image, then the optical flow vector would change continuously across the entire image area; when a moving object is present in the image, the target and the image background would move relative to one another, so the velocity vector of the moving object is inevitably different from the velocity vector of the adjacent background area; thus, the moving object and its location are detected. Image difference methods are relatively straightforward and easy to implement. Therefore, these methods have now become the most widely used to detect moving targets. There are two types of image difference methods: the background subtraction method and the inter-frame difference method. The background subtraction method compares the current frame against the background reference model in a series of images to detect moving objects, and its performance depends on the background modeling technology used. The inter-frame difference method performs a difference operation on two or three adjacent frames in a series of video images to obtain the contour of a moving target.
However, the image difference methods, the background subtraction method and the inter-frame difference method, are highly sensitive to interference factors that cause changes in the scene, for example, lighting, plants swinging in the wind, etc. For example, in a night vision environment, a flying insect may lead to changes in the scene. Since conventional motion detection algorithms are highly sensitive to interference factors that cause changes in the scene, a motion detection alarm is triggered once a change in the scene is detected. However, this type of alarm is not desired; in other words, it is a false positive alarm. Therefore, there is a need to detect and identify moving objects, for example, flying insects, in a night vision environment to avoid false positive alarms.
The disclosed methods and systems address one or more of the problems listed above.